320 citations · 1.7k across the 36 of their papers we have counts for
24 papers · 1 filter
Generative Adversarial Exploration for Reinforcement Learning
Weijun Hong, Menghui Zhu, Minghuan Liu +4
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses o…
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…
Bag of Tricks for Node Classification with Graph Neural Networks
Yangkun Wang, Jiarui Jin, Weinan Zhang +3
Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs. However,…
Towards Generalized Implementation of Wasserstein Distance in GANs
Minkai Xu, Zhiming Zhou, Guansong Lu +3
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it doe…
Model-based Policy Optimization with Unsupervised Model Adaptation
Jian Shen, Han Zhao, Weinan Zhang +1
Model-based reinforcement learning methods learn a dynamics model with real data sampled from the environment and leverage it to generate simulated data to derive an agent. However…
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
Chang Liu, Huichu Zhang, Weinan Zhang +2
The heavy traffic congestion problem has always been a concern for modern cities. To alleviate traffic congestion, researchers use reinforcement learning (RL) to develop better tra…